October 29-November 2, 2017
New York
Delivering on the promise of data science

TRACK TOPICS:

Track 1: BUSINESS
Analytics strategy & operationalization
Track 2: TECH
Predictive modeling &
machine learning methods
Track 3 (Day 1): MARKETING
Marketing & market research analytics
Track 3 (Day 2): CASE STUDIES
Varied business applications

SESSION LEVELS:

All level tracks Blue circle sessions are for All Levels   Red triangle sessions are Expert/Practitioner Level

Agenda Overview – New York 2017
Pre-Conference Workshops: Sunday, October 29, 2017
Full-day Workshop
Big Data: Proven Methods You Need
to Extract Big Value

Vladimir Barash, Graphika
Morning Session Workshop
R Bootcamp: For Newcomers to R
Max Kuhn, RStudio
Full-day Workshop
R for Machine Learning:
A Hands-On Introduction

Max Kuhn, RStudio

DAY 1, Monday, October 30, 2017
(PAW Financial & PAW Healthcare run in parallel on this day - dual registration required)
8:00-8:45am Registration & Networking Breakfast
8:45-8:50am Conference Chair Welcome
Eric Siegel, Predictive Analytics World
8:50-9:40am
KEYNOTE
Analytics for the Job: Tips and Tricks for Success
Anne Robinson, Verizon Wireless
9:40-10:00am Diamond Sponsor Presentation
10:00-10:30am Exhibits & Morning Coffee Break
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling & machine
learning methods
Track 3—MARKETING: Marketing & market research analytics
10:30-11:15am Crisis response; analytics management Hand-labeled training data Churn modeling
Lessons from:
NYC Mayor's Office
Quickly Building an Analytics Environment to Address a Public Health Crisis in NYC
All level tracks
Simon Rimmele, NYC Mayor's Office of Data Analytics
Case Study: Bloomberg L.P.
Crowd-Sourcing and Quality:
How To Get The Best Out of Hand-Tagged Training Data for Machine Learning Models
All level tracks
Leslie Barrett, Bloomberg L.P.
Case Study: Paychex
Retention Modeling in Uncertain Economic Times
All level tracks
Rob Rolleston, Paychex
11:20-11:40am Education and team building Time series modeling Market research and analytics
Lessons from:
LinkedIn
The Sprint for Teaching Data Science: LinkedIn Learning, Analytics, and the New Era of Just-In-Time Skills Training All level tracks

Steve Weiss, LinkedIn
Time Series Prediction with Twitter: A Case Study of Crime in New York City
Anasse Bari, George Washington University
Aaron McKinstry, Courant Institute of Mathematical Sciences of New York University
Chuan-Heng Lin, Enrolled at New York University
Gen Xiang, Trinnacle Capital Management
Case Study: Walmart
Relative Value of Implicit and Explicit Feedback in Predicting Customer Preferences

Jennifer Prendki, Walmart
11:40am-12:00pm Market research and analytics
Case Study: Verizon Wireless
Predicting Brand Love With Wireless Behaviors

Michael Gooch-Breault, Verizon Wireless
12:05-1:30pm Lunch in Exhibit Hall
1:30-2:15pm
KEYNOTE
The Predictability Predicament: Your Model Overlooks the Real Target

Claudia Perlich, Dstillery
2:15-2:35pm Diamond Sponsor Presentation
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling & machine
learning methods
Track 3—MARKETING: Marketing & market research analytics
2:40-3:00pm Analytics strategy Analytical methods Marketing applications
Lessons from:
The Clorox Company
Getting Started with Data Science Driven Insights, Execution and Innovation in the CPG Industry All level tracks

Payel Chowdhury, The Clorox Company
Machine Learning vs. Feature Engineering: What should the Focus be in Attempting to Predict Customer Behaviour
Richard Boire, Environics Analytics
Real-Time Automation to Build Relationships & Retain Customers
Kristina Pototska, TriggMine
3:05pm-3:25pm Acquisition for academic enrollment
Case Study: Becker College
Acquisition Funnel for Higher Education

Feyzi Bagirov, Becker College
3:25-3:55pm Exhibits & Afternoon Break
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling & machine
learning methods
Track 3—MARKETING: Marketing & market research analytics
3:55-4:40pm Analytics strategy Analytical methods Churn modeling; uplift modeling
Lessons from:
Prudential Financial
Value Creation Through Analytics Innovation
All level tracks
Wayne Huang, Prudential Financial
Case Study: Citigroup
A Modified Logistic Regression Approach Enhanced by New Interactions and Scaling Detections through Random Forests and GBM

Yulin Ning, Citigroup
Case Study: The Co-operators
Which Predictive Model Will Best Help Increase Retention?

Emilie Lavoie-Charland, The Co-operators
4:45-5:30pm Building Data Science Teams Forecasting; analytical methods Optimizing outreach; uplift modeling
Lessons from:
Comcast

Accelerating Data Science InnovationAll level tracks
Bob Bress, Comcast
Case Study: Micron Technology
Demand Forecasting with Machine Learning

Colin Ard, Micron Technology
Using Rapid Experiments and Uplift Modeling to Optimize Outreach at Scale
Daniel Porter, BlueLabs
5:30-7:00pm Networking Reception

Go to Top of Page

DAY 2, Tuesday, October 31, 2017
(PAW Financial & PAW Healthcare run in parallel on this day - dual registration required)
8:00-8:35am Registration & Networking Breakfast
8:35-8:40am Conference Chair Welcome
Eric Siegel, Predictive Analytics World
8:40-9:25am Special Plenary Session
What to Optimize? The Heart of Every Analytics Problem
Dr. John Elder, Elder Research, Inc.
9:25-9:40am Plenary Session
Industry Trends: Highlights from the 2017 Data Miner Survey
Karl Rexer, Rexer Analytics
9:40-10:00am Diamond Sponsor Presentation
Move Beyond Basic Targeting and Accelerate Sales with Help from Machine Learning
Kelley Gazdak, Dun & Bradstreet
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling & machine
learning methods
Track 3—MORE CASE STUDIES: Varied business applications
  Getting it deployed Data quality Data storytelling
10:00-10:45am Lessons from:
Honeywell
Operationalizing Analytics:
The Critical Last Mile to Value
All level tracks
William Groves, Honeywell
Three Steps for Improving
Data Quality for Predictive Analytics

Tom Redman, Data Quality Solutions
The Limits of Surveys and the Power of Google Search Data
Seth Stephens-Davidowitz, Author, Everybody Lies and former Google data scientist
10:45-11:15am Exhibits & Morning Coffee Break
11:15-11:35am Workforce analytics Best practices Media Applications
Lessons from:
Intel
How Intel Wins the Right Marketplace Talent with Analytics
All level tracks
Hai Harari, Intel
Q&A: Ask Dean and
Karl Anything (about Best Practices)

Dean Abbott, SmarterHQ
Karl Rexer, Rexer Analytics
Case Study: BBC Worldwide
Catchy content: What makes TV content work?
David Boyle, BBC Worldwide
11:40am-12:00pm Industry-leading case studies
Customer Journey Analytics: Blazing Paths to Customer Success
Steven Ramirez, Beyone the Arc
12:00-1:10pm Lunch in Exhibit Hall
1:10-1:55pm
KEYNOTE
UPS' Road to Optimization

Jack Levis, UPS
1:55-2:15pm Diamond Sponsor Presentation
2:15-3:00pm Expert Panel
Women in Predictive Analytics: Opportunities and Challenges
Moderator: Anne Robinson, Verizon Wireless
Panelists:
Pallavi Yerramilli, The Trade Desk
Tracie Coker Kambies, Deloitte Consulting LLP
3:00-3:30pm Exhibits & Afternoon Break
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling & machine
learning methods
Track 3—MORE CASE STUDIES: Varied business applications
Analytics management Model interpretation PA adoption in a new industry
3:30-3:50pm Lessons from:
Vanguard
Project Management for Data Scientists
All level tracks
Wanda Wang, Vanguard
Case Study: SmarterHQ
When Model Interpretation Matters: Understanding Complex Predictive Models

Dean Abbott, SmarterHQ
Case Study: RightShip
Overcoming Challenges Implementing a Risk Model in the Maritime Industry

Bryan Guenther, RightShip
3:55-4:15pm Agriculture analytics
Case Study: Circle A Farms
Advancing Hydroponics through IoT Analytics
All level tracks
Steve Fowler, Jivoo
4:15-5:00pm Model deployment Data policy Logistics analytics
Lessons from:
John Hancock

A Shiny Way to Operationalizing Analytics All level tracks
Shatrunjai Singh, John Hancock
Regulating Opacity: Solving for the Conflict Between Laws and Analytics
Andrew Burt, Immuta
Case Study: Cargonexx
Leveraging Machine Learning Techniques for Realtime Pricing in B2B Truck Logistics

Alwin Haensel, HAMS

Post-Conference Workshops: Wednesday, November 1, 2017
Full-day Workshop
The Advanced Data Preparation Bootcamp: Whip your Data into Shape
Dean Abbott, Abbott Analytics
Full-day Workshop
The Best and the Worst of Predictive Analytics:
Machine Learning Methods and Common Data Science Mistakes

Dr. John Elder, Elder Research, Inc.
Full-day Workshop
Spark on Hadoop for Machine Learning: Hands-On Lab
James Casaletto, MapR Technologies


Post-Conference Workshop: Thursday, November 2, 2017
Full-day Workshop
Supercharging Prediction with Ensemble Models
Dean Abbott, Abbott Analytics

Go to Top of Page

© 2017 Predictive Analytics World | Privacy
Produced by Prediction Impact, Inc. and Rising Media, Inc.

Predictive Analytics Company           Predictive Analytics Event Producer